ABSTRACT In this paper, we consider the stochastic vehicle routing problem with delivery choice, where, upon reaching a particular customer, the driver has the choice of how much to deliver once the customer demand is revealed. In our earlier work, a basic version of the problem was considered, in which the driver cannot return to the depot midway in a route to replenish the cargo on board (see Venkateshan and Mathur 2025). In the present work, we expand on the choices that the driver has by allowing the driver to return to the depot to replenish before returning to the same customer or to proceed to the next customer. We prove the optimality of different threshold‐based fulfillment policies. Tests show that these policies significantly reduce the computational effort needed to determine the optimal fulfillment policy along a route, as well as its associated expected profit. We also show that a single‐vehicle tour that visits all customers is optimal even if multiple vehicles are allowed.
ABSTRACT Distributed manufacturing enabled by additive manufacturing (AM) allows supply chains to rapidly adapt to changing market conditions through flexible facility relocation and capacity adjustment. However, existing studies largely overlook the logistical aspects of AM‐based supply chains and typically treat facility location and logistics decisions separately. This study addresses this gap by developing a novel AM supply network design model that simultaneously considers the dynamic relocation of AM facilities, the adjustment of production capacities, and delivery routing decisions over multiple periods. We formulate the problem as a mixed‐integer linear programming model (MILP). Given the computational challenges associated with the delivery routing component, we propose a machine learning genetic algorithm (MLGA) approach to solve the problem efficiently. MLGA incorporates a machine learning surrogate to estimate the cost of the capacitated vehicle routing subproblem (CVRP), thereby significantly accelerating the convergence of the genetic algorithm (GA). We train two different machine learning surrogates: One based on a neural network model and the other on an XGBoost model. The MLGA solution methodology, under both surrogate models, performs satisfactorily in terms of solution quality and computation time across a set of randomly generated instances. Extensive sensitivity analyses are also conducted on a realistic use case to evaluate the model's applicability under varying service area, demand, and cost scenarios, offering valuable managerial insights and practical implications.
As online shopping channels become ubiquitous, consumers are faced with a vast number of digital purchasing options. To attract more consumers, many e-shops utilize cashback websites (CWs) to provide online rebates. This study examines a dual-channel supply chain, in which the manufacturer sells products through both its own online store and a downstream e-tailer. Both the manufacturer and the e-tailer need to decide whether to offer rebates via CW. We explore the rebate strategy of the two firms as well as its impacts on prices, profit, supply chain, and consumers. Our results show that (i) low-end consumers' valuation plays a significant role on the equilibrium rebate strategy, and interestingly, a scenario where solely the manufacturer provides rebates never emerges in equilibrium. (ii) An intriguing asymmetry emerges: while manufacturer rebates invariably advantage the e-tailer, e-tailer rebates may benefit/hurt the manufacturer. (iii) As the valuation of low-end consumers increases, the equilibrium rebate strategy initially enhances supply chain performance, then diminishes it, and ultimately has no effect. (iv) Low-end consumers benefit from both manufacturer rebates and e-tailer rebates when their valuation is at a medium level. In contrast, high-end consumers incur losses under manufacturer rebates, while e-tailer rebates may either benefit or disadvantage them. In addition, we extend the model to several alternative scenarios, such as simultaneous pricing, temporary rebates, product returns, and coordinated rebates.
ABSTRACT In reward‐based crowdfunding, asymmetric information about product quality can hinder both fundraising success and post‐campaign sales. To mitigate this problem, Belavina (2020) introduces a target‐based deferred payment mechanism, which withholds any funds raised above a prespecified funding target until the creator delivers the promised product to backers. This paper develops a game‐theoretic signaling model to analyze how this mechanism influences strategic behavior in crowdfunding campaigns. The analysis reveals that target‐based deferred payments not only lower the signaling costs for high‐quality creators but also reshape their optimal signaling strategies. Specifically, when the fixed setup cost of producing a high‐quality product is relatively low, high‐quality creators optimally signal their type by offering low reward prices. Conversely, when the fixed setup cost is high, setting a high funding target becomes the dominant separating strategy. The paper further examines alternative formulations of the deferred payment mechanism and offers practical guidance for creators on designing campaigns that credibly signal product quality.
We study a two-stage flexible flow shop scheduling problem aimed at maximizing the total weight of just-in-time jobs, defined as those that complete exactly on their due dates. Two machine configurations are examined: (i) a single common bottleneck machine at stage 1 and parallel dedicated machines at stage 2; (ii) parallel dedicated machines at stage 1 and a single common bottleneck machine at stage 2, where denotes the number of parallel dedicated machines. For the first configuration, we propose a pseudo-polynomial-time dynamic programming algorithm and a fully polynomial-time approximation scheme when is fixed. For the second configuration, we establish that the problem is strongly & Nscr; & Pscr; -hard for arbitrary , even when all jobs have unit weight, and show that it remains ordinarily & Nscr; & Pscr; -hard for under the same condition. We then design a pseudo-polynomial-time algorithm for fixed .
